Salbutamol is a short-acting β₂-adrenergic agonist used to provide acute relief of symptoms of asthma and chronic obstructive pulmonary disease (COPD). As it is a widely prescribed drug, it serves as a useful indicator to make inferences on respiratory health and disease burden across Scotland. This report aims to explore seasonal, geographical and socioeconomic factors in Salbutamol prescription trends throughout Scotland in 2024, using prescribing data by NHS Scotland health board sourced from Public Health Scotland.
Based on current knowledge, we can expect to find:
Increased prescription numbers towards the winter months, as cold air is a trigger for asthma.
A potential rise in spring due to increased allergens (e.g. pollen) triggering asthma symptoms.
Health boards with colder climates (such as NHS Highland) or those who treat a more deprived population (such as NHS Lanarkshire) to have a higher prescription rate.
# Packages Used
library(tidyverse) # For clean data visualisation and manipulation
library(janitor) # For consistent formatting of datasets
library(gt) # For creating gt tables
library(here) # For reproducible file paths
library(sf) # For working with spatial data
library(plotly) # For interactive plots# Data wrangling to produce a dataset only featuring data relating to bronchodilator prescriptions in Scotland.
# Filter the prescription datasets, for bronchodilators using its BNF code (0301).
jantojun_2024 <- read_csv("https://www.opendata.nhs.scot/dataset/84393984-14e9-4b0d-a797-b288db64d088/resource/f0df380b-3f9b-4536-bb87-569e189b727a/download/hb_pitc2024_01_06-1.csv") %>%
clean_names() %>%
filter(str_starts(bnf_item_code, "0301"))
jultodec_2024 <- read_csv("https://www.opendata.nhs.scot/dataset/84393984-14e9-4b0d-a797-b288db64d088/resource/f3b9f2e2-66c0-4310-9b8e-734781d2ed0a/download/hb_pitc2024_07_12-1.csv") %>%
clean_names() %>%
filter(str_starts(bnf_item_code, "0301"))
# Combine the two datasets into one to cover the full year of 2024
data_2024 <- bind_rows(jantojun_2024, jultodec_2024)
# Read health board lookup table, and remove out old health board codes by keeping NA values for 'hb_date_archived'
hb_names <- read_csv("https://www.opendata.nhs.scot/dataset/9f942fdb-e59e-44f5-b534-d6e17229cc7b/resource/652ff726-e676-4a20-abda-435b98dd7bdc/download/hb14_hb19.csv") %>%
clean_names() %>%
filter(is.na(hb_date_archived))
# Assigns the names of the health boards to its corresponding HBT numeric code by joining with the health board look up table.
data_2024_with_names <-
full_join(hb_names, data_2024, by = c("hb" = "hbt")) %>%
filter(!is.na(hb_name))
# Exclude unneeded columns in the dataset.
bronchodilators_2024 <- data_2024_with_names %>%
select(-hb_date_enacted, -hb_date_archived, -country, -dmd_code)# Assessing which bronchodilators are the most widely prescribed.
# Finds the top 5 most prescribed drugs amongst the bronchodilators.
top5_bronchodilators <- bronchodilators_2024 %>%
mutate(drug_type = if_else(str_starts(bnf_item_code, "0301011R0"),
"Salbutamol", "Other")) %>%
# Assigns drugs into salbutamol or other. Useful for a creating colour coded bar chart.
group_by(bnf_item_description, drug_type) %>%
summarise(paid_items_total = sum(number_of_paid_items),
.groups = "drop") %>%
slice_max(paid_items_total, n = 5)
# Creates a bar chart showing top 5 most prescribed bronchodilators.
count_plot <- top5_bronchodilators %>%
ggplot(aes(x= paid_items_total,
y= reorder(str_wrap(bnf_item_description,
width = 30),
paid_items_total),
fill = drug_type,
text = paste0("Prescriptions: ", paid_items_total))) +
# Adds text information for interactive graph
geom_col() +
scale_fill_manual(values = c("Salbutamol" = "steelblue",
"Other" = "grey"),
name = "Drug Type") +
labs(title = "Top 5 Most Prescribed\n Bronchodilators",
#\n serves as a line break
subtitle = "Scotland (2024)",
x = "Prescriptions Issued",
y = NULL) +
theme_minimal() +
theme(plot.title = element_text(face = "bold"))
# Makes graph interactive
ggplotly(count_plot, tooltip = "text")Figure 1. Interactive bar chart. Hover mouse over bars to see exact prescription numbers.
Based on the graph, Salbutamol-based drugs are dominant in this ranking, with ranks 1, 2 and 5 all representing formulations of Salbutamol. This highlights the widespread usage of Salbutamol in Scotland as well as suggesting a high prevalence of asthma and COPD in the country. It also reflects its role in first line therapy in the management of acute symptoms of asthma recommended by NICE (2025), and its usage as a short acting bronchodilator for breathlessness in COPD, as recommended by the NHS (2023).
# Assessing monthly patterns in Salbutamol prescriptions.
# Filters data for only Salbutamol-based drugs, using its BNF code to ensure brand names such as 'Ventolin' are not excluded.
salbutamol <- bronchodilators_2024 %>%
filter(str_detect(bnf_item_code, "0301011R0"))
# Creates a dataset to show number of Salbutamol prescriptions per month.
salbutamol_monthly <- salbutamol %>%
# Change 'paid_date_month' into a factor.
mutate(
paid_date_month = parse_date_time(paid_date_month, "ym"),
month = factor(format(paid_date_month, "%B"),
#%B formats date to show full month name
levels = month.name)) %>%
group_by(month) %>%
summarise(paid_items_total = sum(number_of_paid_items))
# Creates a line graph to see monthly prescription trends
monthly_plot <- salbutamol_monthly %>%
ggplot(aes(x = month, y = paid_items_total, group = 1,
text = paste0(month, "<br>", "Prescriptions: ", paid_items_total))) + # Add text information for interactive graph.
geom_line(colour = "steelblue") +
geom_point(colour = "#2E5D91", size = 2) +
labs(title = "Salbutamol Prescriptions per Month",
subtitle = "Scotland (2024)",
x = "Month",
y = "Prescriptions Issued") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 25),
plot.title = element_text(face = "bold"))
# Make it have interactive features
ggplotly(monthly_plot, tooltip = "text")Figure 2. Interactive line graph. Hover mouse over line graph to see exact figures.
The line graph shows an increase in prescriptions during the winter months, particularly in December. This likely reflects the seasonal exacerbations in asthma and COPD symptoms triggered by colder weather. The NHS recognises cold air and infection as triggers for asthma symptoms (NHS, 2025).
Additionally, the graph shows a spike in prescriptions during May. This may be due to higher pollen levels in spring, which is also recognised by the NHS as a trigger for asthma symptoms (NHS, 2025).
This section aims to explore the geographical and socioeconomic variation in Salbutamol prescription trends.
Deprivation levels were based on the latest data from Scottish Index of Multiple Deprivation (SIMD) in 2020. SIMD bases deprivation of seven domains: income, employment, education, health, access to services, crime and housing. Deprivation levels are split into deciles from 1-10, with decile 1 being most deprived and decile 10 being least deprived. As SIMD data is split across 6,976 areas, the average decile was taken for each health board (Scottish Government, 2020).
# Data wrangling to produce Salbutamol prescriptions data based on population of health boards across Scotland (2024).
# Dataset with population estimates of each health board (1981-2024)
hb_population <- read_csv("https://www.opendata.nhs.scot/dataset/7f010430-6ce1-4813-b25c-f7f335bdc4dc/resource/27a72cc8-d6d8-430c-8b4f-3109a9ceadb1/download/hb2019_pop_est_03102025.csv") %>%
clean_names()
# Filter for only 2024
pop_2024 <- hb_population %>%
filter(year == "2024")
# Create dataset with the populations for each healthboard for 2024.
pop_2024_with_names <-
full_join(hb_names, pop_2024, by = c("hb" = "hb")) %>%
rename(population_2024 = "all_ages") %>%
filter(!is.na(hb_name), sex == "All") %>%
select(hb_name, population_2024)
# Creates a dataset to show number of prescriptions issued for each healthboard.
salbutamol_byhb <- salbutamol %>%
group_by(hb_name) %>%
summarise(paid_items_total = sum(number_of_paid_items)) %>%
rename(prescriptions = "paid_items_total")
# Add population data and new column for prescriptions per 100k.
salbutamol_population <-
full_join(salbutamol_byhb, pop_2024_with_names,
by = c("hb_name" = "hb_name")) %>%
mutate(prescriptions_per_100k
= prescriptions/population_2024*100000) %>%
arrange(desc(prescriptions_per_100k)) %>%
mutate(Rank = row_number()) %>%
select(Rank, hb_name, prescriptions_per_100k, prescriptions, population_2024)# Read SIMD data and summarise each health board's average SIMD.
simd_average <- read_csv("https://www.opendata.nhs.scot/dataset/78d41fa9-1a62-4f7b-9edb-3e8522a93378/resource/acade396-8430-4b34-895a-b3e757fa346e/download/simd2020v2_22062020.csv") %>%
select(HB, SIMD2020V2CountryDecile) %>%
group_by(HB) %>%
summarise(average_decile = mean(SIMD2020V2CountryDecile)) %>%
mutate(average_decile = round(average_decile, digits = 1))
# Join with health board names.
hb_simd <-
full_join(simd_average, hb_names, by = c("HB" = "hb")) %>%
select(hb_name, average_decile)
# Add SIMD averages to Salbutamol population dataset.
salbutamol_data <-
full_join(salbutamol_population, hb_simd, by = "hb_name")# Assessing Salbutamol prescriptions trends by health board with a gt table.
table <- salbutamol_data %>%
arrange(Rank) %>%
gt() %>%
cols_label(hb_name = "Health Board",
prescriptions = "Prescriptions Issued",
population_2024 = "Population",
prescriptions_per_100k = "Prescriptions per 100k",
average_decile = "Average SIMD Decile (1-10)") %>%
tab_header(title = "Salbutamol Prescriptions per Capita Ranked by Health Board",
subtitle = "Scotland (2024)") %>%
tab_style(list(cell_fill(color = "steelblue")),
locations = cells_title()) %>%
tab_style(list(cell_text(weight = "bold", color = "white")),
locations = cells_title()) %>%
fmt_number(columns = c(population_2024, prescriptions, prescriptions_per_100k), decimals = 0) %>%
cols_align(align = "right",
columns = c(prescriptions_per_100k:average_decile)) %>%
cols_align(align = "center", columns = Rank) %>%
grand_summary_rows(columns = c(prescriptions_per_100k, prescriptions, average_decile),
fns = list("Average"= ~mean(.,na.rm = TRUE)),
fmt = list(~ fmt_number(.,decimals = 1))) %>%
tab_source_note(source_note = "Source: Public Health Scotland")
table| Salbutamol Prescriptions per Capita Ranked by Health Board | ||||||
| Scotland (2024) | ||||||
| Rank | Health Board | Prescriptions per 100k | Prescriptions Issued | Population | Average SIMD Decile (1-10) | |
|---|---|---|---|---|---|---|
| 1 | NHS Dumfries and Galloway | 61,686 | 89,975 | 145,860 | 5.3 | |
| 2 | NHS Lanarkshire | 54,136 | 367,348 | 678,570 | 4.7 | |
| 3 | NHS Ayrshire and Arran | 50,536 | 185,846 | 367,750 | 4.6 | |
| 4 | NHS Western Isles | 50,242 | 13,073 | 26,020 | 5.1 | |
| 5 | NHS Greater Glasgow and Clyde | 49,948 | 607,997 | 1,217,270 | 4.7 | |
| 6 | NHS Borders | 46,454 | 54,342 | 116,980 | 5.8 | |
| 7 | NHS Fife | 44,250 | 165,833 | 374,760 | 5.4 | |
| 8 | NHS Forth Valley | 42,180 | 129,214 | 306,340 | 5.6 | |
| 9 | NHS Tayside | 41,492 | 173,896 | 419,110 | 5.6 | |
| 10 | NHS Highland | 41,255 | 134,070 | 324,980 | 5.6 | |
| 11 | NHS Shetland | 35,019 | 8,121 | 23,190 | 6.4 | |
| 12 | NHS Lothian | 34,238 | 319,159 | 932,180 | 6.3 | |
| 13 | NHS Grampian | 33,842 | 200,300 | 591,870 | 6.7 | |
| 14 | NHS Orkney | 30,777 | 6,777 | 22,020 | 6.1 | |
| Average | — | — | 44,003.9 | 175,425.1 | — | 5.6 |
| Source: Public Health Scotland | ||||||
Figure 3. Table ranking total the Salbutamol prescriptions per 100k people by Scottish health board. Note that an individual may have multiple prescriptions.
# Assessing Salbutamol prescription trends by NHS health board population by creating a map.
# Read NHS health board shapes for map.
# Data taken from: https://www.data.gov.uk/dataset/27d0fe5f-79bb-4116-aec9-a8e565ff756a/nhs-health-boards-scotland
map <- st_read(here("data", "Week6_NHS_healthboards_2019.shp"),
quiet = TRUE) %>%
mutate(HBName = paste0("NHS ", HBName)) # add "NHS" in front of HBName to match 'salbutamol_population' dataset for joining.
# Join with map data.
prescription_mapped <-
full_join(map, salbutamol_data, by = c("HBName" = "hb_name")) %>% filter(!is.na(HBCode))
# Create function for the maps as multiple will be created for visualisation
map_function <- function(fill_change, label){prescription_mapped %>%
ggplot(aes(fill = {{fill_change}},
text = paste0(HBName, "<br>", label, ": ",
round({{fill_change}}, 1)))) +
geom_sf(colour = "black", size = 0.1) +
theme_void() +
theme(plot.title = element_text(face = "bold"),
legend.title = element_text(size = 10))}
#Creates map of Prescription rates for each health board.
prescription_map <- map_function(fill_change = prescriptions_per_100k,
label = "Prescriptions per 100k") +
scale_fill_distiller(palette = "Blues",
direction = 1,
name = "Prescriptions per 100k") +
labs(title = "Salbutamol Prescriptions by\n NHS Healthboard (2024)",
subtitle = "Rate per 100k people") # \n serves as a line break
ggplotly(prescription_map, tooltip = "text")Figure 4a. Map visualising prescription rates across NHS Scotland health boards (darker blue indicates a higher prescription rate).
# Creates map of SIMD decile averages for each health board
simd_map <-
map_function(fill_change = average_decile,
label = "Average Decile") +
scale_fill_distiller(palette = "RdBu",
direction = 1,
name = "Average SIMD Decile",
limits = c(3.5, 8)) +
labs(title = "Average Deprivation Level by\n NHS Health Board (SIMD 2020)",
subtitle = "Scotland")
ggplotly(simd_map, tooltip = "text")Figure 4b. Average SIMD decile across NHS Scotland health boards (red = more deprived, blue = less deprived).
Dumfries and Galloway interestingly has the highest prescription rate, despite not having the lowest average SIMD decile or a cold climate relative to other areas. However, a potential reason for this may be that Dumfries and Galloway has the highest proportion of the population aged 65+ amongst all Scottish council areas in 2024 (National Records of Scotland, 2025). With COPD prevalence increasing with age and asthma management being more intensive, bronchodilators such as Salbutamol may have a higher demand in elderly populations.
Figure 3 and the maps from figure 4 do still demonstrate an overall trend where areas with higher deprivation have higher prescription rates for Salbutamol, even with the modest range of the average SIMD deciles across the different health boards. In figure 3, the four lowest ranked health boards all have an average decile of 6.0 or above. A lower SES is linked to higher rates of asthma and COPD due to factors such as increased smoking and poorer housing conditions can drive demand for Salbutamol.
This report has some limitations, and findings should be approached with a degree of caution.
Prescribing data does not fully reflect actual drug consumption levels.
Latest data for SIMD was last published in 2020. With the report focusing on prescription trends in 2024, there may be some changes in deprivation levels across the health boards between then and now.
There was not drastic variation amongst average SIMD deciles between different health boards. To get a true idea of how deprivation influences prescription rates, it would be more beneficial to analyse SIMD data of each area code, where SIMD varies greatly.
Continuous monitoring of Salbutamol prescriptions is necessary to track respiratory health in Scotland. Future reports can analyse how trends in age structure or smoking rates affect prescription rates. With climate change becoming an increasing concern worldwide, metered-dose inhalers are increasingly being replaced by other more environmentally friendly such options as dry-powder inhalers. Future research can explore alternative forms of salbutamol or other bronchodilators being prescribed in efforts to combat climate change.
Through analysis of prescription data from Public Health Scotland, we can see that Salbutamol is a top choice amongst prescribers throughout Scotland to provide acute symptomatic relief for asthma and COPD.
Analysis of seasonal trends in Salbutamol reveal increases in prescriptions in May and winter months, keeping consistent with pollen and cold air being known triggers of asthma.
Geographically, prescription rates were varied across the health boards, with a general trend of increased prescription rate in health boards serving more deprived populations. This keeps in line with our established knowledge of how lower SES can fuel health inequalities and impact respiratory health outcomes.
Overall, the prescription trends highlight that population respiratory health is complex and multi-factorial. By further collecting and studying data, we can develop a deeper understanding to what drives an increased prevalence for conditions like asthma and COPD, and continue to develop strategies to improve respiratory health outcomes at a population level.
Asthma - NHS (2025). Available at: https://www.nhs.uk/conditions/asthma/ (Accessed: 12 November 2025).
Chronic obstructive pulmonary disease (COPD) - NHS (2023). Available at: https://www.nhs.uk/conditions/chronic-obstructive-pulmonary-disease-copd/treatment/ (Accessed: 12 November 2025).
Asthma, acute | Treatment summaries | BNF content published by NICE (2025). Available at: https://bnf.nice.org.uk/treatment-summaries/asthma-acute/ (Accessed: 12 November 2025).
Mid-2024 population estimates - National Records of Scotland (NRS) (2025). Available at: https://www.nrscotland.gov.uk/publications/mid-2024-population-estimates/ (Accessed: 12 November 2025).
Scottish Index of Multiple Deprivation 2020 (no date). Available at: https://www.gov.scot/collections/scottish-index-of-multiple-deprivation-2020/ (Accessed: 12 November 2025).